Dual y-axis for different scales in Matplotlib - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a dual y-axis plot in matplotlib?

A dual y-axis plot shows two different y-axes on the same graph, each with its own scale. This helps compare two datasets with different units or ranges on one plot.

beginner

Which matplotlib function creates a second y-axis sharing the same x-axis?

The twinx() function creates a second y-axis on the right side of the plot that shares the same x-axis as the original plot.

beginner

Why use different colors for lines and y-axis labels in a dual y-axis plot?

Using different colors helps clearly link each line to its corresponding y-axis, making the plot easier to read and understand.

beginner

How do you label the second y-axis in matplotlib?

After creating the second y-axis with twinx(), use ax2.set_ylabel('label') to add a label to the right y-axis.

beginner

What is a common use case for dual y-axis plots?

They are useful when comparing two related datasets with different units or scales, like temperature and rainfall over time.

Which matplotlib method creates a second y-axis sharing the same x-axis?

A plot()

B twiny()

C twinx()

D subplot()

The twinx() method creates a second y-axis on the right side sharing the x-axis.

Why use a dual y-axis plot?

A To compare two datasets with different y-axis scales

B To plot two datasets with the same scale

C To create a 3D plot

D To plot histograms

Dual y-axis plots help compare datasets with different units or scales on the same x-axis.

How do you label the second y-axis in matplotlib?

A ax.set_xlabel()

B ax.set_ylabel()

C plt.ylabel()

D ax2.set_ylabel()

After creating the second y-axis with twinx(), use ax2.set_ylabel() to label it.

What is a good practice to distinguish lines in a dual y-axis plot?

A Use different colors for lines and y-axis labels

B Use dashed lines only

C Use the same color for both lines

D Use no labels

Different colors help link each line to its y-axis, improving readability.

Which side of the plot does the second y-axis appear by default?

A Left

B Right

C Top

D Bottom

The second y-axis created by twinx() appears on the right side.

Explain how to create a dual y-axis plot in matplotlib and why it is useful.

Think about plotting two lines with different units on one graph.

Describe best practices for making dual y-axis plots easy to read.

Consider how to help the viewer quickly understand which line belongs to which axis.

Practice

1. What is the main purpose of using a dual y-axis in a matplotlib plot?

easy

A. To compare two datasets with different scales on the same plot

B. To create two separate plots side by side

C. To add a legend to the plot

D. To change the color of the plot lines

Solution

1. Step 1: Understand the purpose of dual y-axis

   A dual y-axis allows plotting two datasets with different value ranges on the same graph for easy comparison.

2. Step 2: Identify the correct use case

   Comparing datasets with different scales requires two y-axes; other options do not describe this purpose.

3. Final Answer:

   To compare two datasets with different scales on the same plot -> Option A

4. Quick Check:

   Dual y-axis = compare different scales [OK]

Hint: Dual y-axis means two y-scales on one graph [OK]

Common Mistakes:

• Thinking dual y-axis creates two separate plots
• Confusing dual y-axis with adding legends
• Assuming it changes line colors only

2. Which matplotlib function is used to create a second y-axis sharing the same x-axis?

easy

A. plt.subplots()

B. ax.twiny()

C. ax.twinx()

D. plt.plot()

Solution

1. Step 1: Recall matplotlib methods for axes

   The method twinx() creates a second y-axis sharing the x-axis.

2. Step 2: Differentiate from similar methods

   twiny() creates a second x-axis, not y-axis; subplots() creates figure and axes; plot() draws lines.

3. Final Answer:

   ax.twinx() -> Option C

4. Quick Check:

   Second y-axis = twinx() [OK]

Hint: Use ax.twinx() for second y-axis, twiny() is for x-axis [OK]

Common Mistakes:

• Using plt.subplots() to create second y-axis
• Confusing twinx() with twiny()
• Trying to use plt.plot() to add axes

3. What will the following code output?

import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.plot([1, 2, 3], [10, 20, 30], 'g-')
ax2.plot([1, 2, 3], [100, 200, 300], 'b-')
print(len(fig.axes))

medium

A. 2

B. 1

C. 3

D. 4

Solution

1. Step 1: Understand axes creation

   plt.subplots() creates one axes (ax1), then ax1.twinx() adds a second y-axis (ax2), so total axes are 2.

2. Step 2: Check figure axes count

   fig.axes is a list of all axes in the figure; after adding ax2, length is 2.

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Two axes after twinx() = 2 [OK]

Hint: twinx() adds one more axes, so total axes count increases by 1 [OK]

Common Mistakes:

• Assuming twinx() replaces the original axes
• Thinking twinx() adds multiple axes
• Confusing axes count with number of plots

4. Identify the error in this code snippet for creating dual y-axis:

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax2 = plt.twinx()
ax.plot([1,2,3], [10,20,30])
ax2.plot([1,2,3], [100,200,300])
plt.show()

medium

A. plt.show() is missing

B. plt.subplots() should be plt.figure()

C. ax2.plot() should be ax.plot()

D. plt.twinx() should be ax.twinx()

Solution

1. Step 1: Check method usage for second y-axis

   twinx() is a method of an axes object, not plt module. So plt.twinx() is invalid.

2. Step 2: Correct method call

   Use ax.twinx() to create the second y-axis sharing the x-axis.

3. Final Answer:

   plt.twinx() should be ax.twinx() -> Option D

4. Quick Check:

   twinx() is an axes method, not plt function [OK]

Hint: twinx() must be called on axes object, not plt [OK]

Common Mistakes:

• Calling twinx() on plt instead of axes
• Confusing figure and axes creation methods
• Missing plt.show() (though present here)

5. You want to plot temperature (°C) and rainfall (mm) over months on the same plot with dual y-axes. Which of these code snippets correctly sets different y-axis labels and colors for clarity?

hard

A. ax1.set_xlabel('Temperature (°C)', color='red')\nax2.set_xlabel('Rainfall (mm)', color='blue')

B. ax1.set_ylabel('Temperature (°C)', color='red')\nax2.set_ylabel('Rainfall (mm)', color='blue')

C. ax1.set_ylabel('Rainfall (mm)', color='blue')\nax2.set_ylabel('Temperature (°C)', color='red')

D. ax1.set_title('Temperature and Rainfall')\nax2.set_title('Temperature and Rainfall')

Solution

1. Step 1: Assign correct y-axis labels

   Temperature and rainfall are y-axis data, so use set_ylabel() on each axis with matching labels.

2. Step 2: Use colors to distinguish axes

   Setting label colors helps visually separate the two y-axes for clarity.

3. Final Answer:

   ax1.set_ylabel('Temperature (°C)', color='red')\nax2.set_ylabel('Rainfall (mm)', color='blue') -> Option B

4. Quick Check:

   Correct y-labels and colors = ax1.set_ylabel('Temperature (°C)', color='red')\nax2.set_ylabel('Rainfall (mm)', color='blue') [OK]

Hint: Label y-axes with units and colors for clear dual-axis plots [OK]

Common Mistakes:

• Setting x-axis labels instead of y-axis
• Swapping labels between axes
• Using titles instead of axis labels